code: def test_quantize_openvino_fp16(self):
      model = mobilenet_v3_small(num_classes=10)
  
      x = torch.rand((10, 3, 256, 256))
      x2 = torch.rand((1, 3, 256, 256))
  
      ov_fp16_model = InferenceOptimizer.quantize(
          model,
          accelerator="openvino",
          input_sample=x,
          precision="fp16",
      )
  
      with InferenceOptimizer.get_context(ov_fp16_model):
          preds1 = ov_fp16_model(x).numpy()
          ov_fp16_model(x2).numpy()  # test dinamic shape
  
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(ov_fp16_model, tmp_dir_name)
          load_model = InferenceOptimizer.load(tmp_dir_name)
  
      with InferenceOptimizer.get_context(load_model):
          preds2 = load_model(x).numpy()
  
      np.testing.assert_almost_equal(preds1, preds2, decimal=5)
  
imports: from unittest import TestCase
  from torchmetrics import F1Score
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import InferenceOptimizer
  from torchvision.models.mobilenetv3 import mobilenet_v3_small
  from torchvision.models import resnet50
  import torch
  from torch import nn
  from torch.utils.data.dataset import TensorDataset
  from torch.utils.data.dataloader import DataLoader
  import tempfile
  import pytest
  import numpy
label_desc: Use image node_id 12 for calling the TestOpenVINO obj's underlying member method code with example usage: obj.test_quantize_openvino_fp16() without return types